Never Retreat, Never Retract: Argumentation Analysis for Political Speeches

Stefano Menini 1 Elena Cabrio 2 Sara Tonelli 1 Serena Villata 2, 3
2 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : In this work, we apply argumentation mining techniques, in particular relation prediction, to study political speeches in monological form, where there is no direct interaction between opponents. We argue that this kind of technique can effectively support researchers in history, social and political sciences, which must deal with an increasing amount of data in digital form and need ways to automatically extract and analyse argumentation patterns. We test and discuss our approach based on the analysis of documents issued by R. Nixon and J. F. Kennedy during 1960 presidential campaign. We rely on a supervised classifier to predict argument relations (i.e., support and attack), obtaining an accuracy of 0.72 on a dataset of 1,462 argument pairs. The application of argument mining to such data allows not only to highlight the main points of agreement and disagreement between the candidates' arguments over the campaign issues such as Cuba, disarmament and health-care, but also an in-depth argumentative analysis of the respective viewpoints on these topics.
Document type :
Conference papers
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01876442
Contributor : Serena Villata <>
Submitted on : Tuesday, September 18, 2018 - 2:26:00 PM
Last modification on : Saturday, January 5, 2019 - 1:09:27 AM

Identifiers

  • HAL Id : hal-01876442, version 1

Collections

Citation

Stefano Menini, Elena Cabrio, Sara Tonelli, Serena Villata. Never Retreat, Never Retract: Argumentation Analysis for Political Speeches. AAAI 2018 - 32nd AAAI Conference on Artificial Intelligence, Feb 2018, New Orleans, United States. pp.4889-4896. ⟨hal-01876442⟩

Share

Metrics

Record views

157